A bearing fault feature extraction method based on vmd parameter optimization

An extraction method and fault feature technology, which is applied in the field of bearing fault feature extraction based on VMD parameter optimization, can solve the problems of parameter dependence on experience acquisition, and the effect of decomposition has a large influence, so as to reduce the influence of noise and vibration frequency bands, increase Implementability, effect of reducing program runtime

Active Publication Date: 2021-10-08
SHANGHAI UNIVERSITY OF ELECTRIC POWER
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Problems solved by technology

However, the modal number and penalty item parameters required for VMD depend on experience, and the selection of parameters has a great influence on the decomposition effect

Method used

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  • A bearing fault feature extraction method based on vmd parameter optimization
  • A bearing fault feature extraction method based on vmd parameter optimization
  • A bearing fault feature extraction method based on vmd parameter optimization

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Experimental program
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Embodiment

[0065] Such as figure 1 As shown, a bearing fault feature extraction method based on VMD parameter optimization, the method includes the following steps:

[0066] (1) Obtain the original vibration signal of the bearing;

[0067] (2) Mode decompose and reconstruct the original vibration signal of the bearing to obtain the reconstructed signal;

[0068] (3) Obtain the modal number and secondary penalty parameters of VMD decomposition according to the original vibration signal of the bearing;

[0069] (4) VMD decomposition is performed on the reconstructed signal by using the obtained modal number and the secondary penalty parameter to obtain the bearing fault characteristic frequency.

[0070] Step (2) The fast local mean empirical mode decomposition method is used to decompose the original vibration signal of the bearing.

[0071] Step (2) performs fast local mean empirical mode decomposition on the original vibration signal of the bearing as follows:

[0072] (a1) Obtain a...

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Abstract

The invention relates to a bearing fault feature extraction method based on VMD parameter optimization, the method comprising the following steps: (1) obtaining the original vibration signal of the bearing; (2) performing modal decomposition and reconstruction on the original vibration signal of the bearing to obtain a reconstructed signal (3) Obtain the modal number and secondary penalty parameters of VMD decomposition according to the original vibration signal of the bearing; (4) use the acquired modal numbers and secondary penalty parameters to perform VMD decomposition on the reconstructed signal to obtain the bearing fault characteristic frequency. Compared with the prior art, the invention can reduce the influence of noise and vibration frequency bands, and effectively extract fault characteristic frequencies.

Description

technical field [0001] The invention relates to a bearing fault feature extraction method, in particular to a bearing fault feature extraction method based on VMD parameter optimization. Background technique [0002] Bearings are important mechanical components in rotating machinery, and their operating conditions directly affect the performance of the equipment. When a bearing fails, its vibration signal is generally characterized by non-stationary and nonlinear characteristics due to the influence of environmental noise and structural deformation. How to extract characteristic fault information from these signals becomes the key to bearing fault diagnosis. [0003] At present, methods for signal processing mainly include time domain, frequency domain and time-frequency domain analysis. Due to the consideration of both time and frequency, time-frequency domain analysis is widely used, such as short-time Fourier transform, wavelet transform and S transform. These methods n...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/00G01M13/045
CPCG01M13/045G06F2218/08G06F2218/12
Inventor 张栋良李帅位钱虹苏晓燕杨婷
Owner SHANGHAI UNIVERSITY OF ELECTRIC POWER
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